A curated collection of machine learning projects

Every day, interesting machine learning projects get posted to GitHub, but they soon disappear into the abyss if you're not quick to bookmark them. I've created a small collection of ML projects that stood out to me from HN, reddit, and GitHub. Preference was given to open source projects witht an online demo. Let me know if I missed any!

Radically efficient machine teaching

Prodigy is a new annotation tool for creating training and evaluation data for machine learning models. It comes with an extensible, self-hosted back-end, active learning-powered models that update as you annotate, and a modern web application that helps you stay focused.

Understand how A.I. will change your future.

Sam DeBrule put together this newsletter which gives you all the best ML/AI reading from the past week. There is also a Medium publication you can follow and also Sam's article on "The Non-Technical Guide To Machine Leaning/AI" is awesome!

Curated Machine Learning models to ⚡supercharge⚡your product

ModelDepot is aiming to make advancements in machine learning more accessible to engineers.

Effortlessly find and use curated pre-trained models to build better products that are augmented by cutting edge strides in the field.

Medium

In traditional supervised machine learning, we teach a model to become more successful and efficient at a task, by providing it with example data. Generally, once the model begins to perform well on the training data for the domain or problem it is tasked with, we expect a reasonable performance for new data.

Medium

We previously talked about how Transfer Learning will radically change ML. People seemed to love it, so we thought we'd follow up with some thoughts on democratizing AI and Software 2.0. Machine Learning has had significant impact on the technology industry in the past few years, but many engineers still view it as little more than a highly specialized tool …See more

Real-time Shake Shack line counter using machine learning

Count is a machine learning company looking to quantify the organic data inside photos, video, and sound. Anybody that has lived in Manhattan understands the dynamics of the Shake Shack line in Madison Square Park. We thought it would be fun to practice our craft quantifying the Shake Shack line in real-time as our first experiment.